Evaluating Social Resource Networks for Community-Driven Waste Valorization, Resource Sharing, and Sustainable Circular Economy Transitions

Author

Bushel Habiba

Abstract

 Rapid electrification, decarbonization, and digital transformation are increasing dependence on renewable energy while creating new requirements for reliable power conversion within energy-sensitive infrastructures. Banking facilities are particularly demanding because data centers, automated transaction systems, communication networks, security platforms, and digital payment services require continuous, high-quality electricity despite fluctuations in renewable generation and grid conditions. This study proposes a machine-learning-optimized intelligent power-converter framework for reliable renewable energy integration in banking infrastructures. The framework combines renewable generation, grid, energy-storage, load, converter, and environmental data to support predictive converter control and adaptive energy management. Machine learning models forecast renewable availability and banking load demand, detect converter anomalies, estimate component degradation, and dynamically optimize switching, voltage regulation, power-factor correction, harmonic mitigation, and battery coordination. Multi-objective optimization balances conversion efficiency, power quality, thermal stress, equipment reliability, renewable utilization, and operational continuity under changing conditions. Real-time monitoring and feedback continuously refine control decisions and predictive maintenance schedules. The proposed approach strengthens converter resilience, reduces energy losses and unplanned interruptions, improves renewable-energy utilization, and provides a scalable pathway toward reliable, efficient, and intelligent low-carbon banking power infrastructures.

Keywords

Intelligent Power Converters; Machine Learning; Renewable Energy Integration; Banking Infrastructure; Predictive Control; Power System Reliability

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References

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